Local campaing’s results (PhD Chapter 1)

This series of files compile all analyses done during Chapter 1 for the local campaign (2014):

All analyses have been done with PRIMER-e 6 and R 3.6.0.

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Caracteristics of each campaign

2014 2016 2017
Sampling date August-September June to August July
Criteria for perturbation Potentially impacted if close to the city or industries, References outside the bay Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria
Regions considered BSI BSI, CPC, BDA, MR BSI, MR
Number of sampled stations 40 (20 HI, 20 R) 78 (26 BSI, 19 CPC, 18 BDA, 15 MR) 126 (111 BSI, 15 MR)
Parameters sampled Organic matter yes yes yes
Photosynthetic pigments no yes yes
Sediment grain-size yes yes yes
Heavy-metals yes yes (for a limited number of stations) no (interpolated based on 2014 and 2016 values)
Benthic communities Compartment targeted Macro-infauna Macro-infauna Macro-infauna
Sieved used 500 µm 1 mm 500 µm and 1 mm
Conservation technique Formaldehyle Formaldehyle Formaldehyle
Others N.A. N.A. N.A.

We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.

Selected variables for the analyses:

Abundances of Bipalponephtys neotena (Bneo) and Spisula solidissima (Ssol) were also considered (see IndVal and SIMPER results).

Statistics for each variable considered:

  Min Max Median Mean SD SE 95% CI
depth 4.000 9.600 7.250 6.970 1.611 0.255 0.499
om 0.187 8.260 0.868 1.368 1.465 0.232 0.454
gravel 0.000 0.481 0.000 0.017 0.076 0.012 0.024
sand 0.000 1.000 0.000 0.148 0.358 0.057 0.111
silt 0.000 0.022 0.001 0.004 0.006 0.001 0.002
clay 0.000 1.000 0.992 0.830 0.361 0.057 0.112
arsenic 1.100 6.000 2.250 2.720 1.259 0.199 0.390
cadmium 0.030 0.220 0.110 0.116 0.045 0.007 0.014
chromium 10.900 143.300 63.200 65.520 29.623 4.684 9.180
copper 2.200 32.400 7.300 11.045 8.675 1.372 2.688
iron 14089.920 188857.220 60284.230 64222.926 31677.444 5008.644 9816.761
manganese 251.670 5962.190 1106.625 1412.044 1050.987 166.176 325.698
mercury 0.000 0.250 0.000 0.014 0.043 0.007 0.013
lead 1.020 12.180 3.110 4.308 2.945 0.466 0.913
zinc 15.900 101.500 45.150 53.163 23.870 3.774 7.397
S 5.000 28.000 14.000 16.275 7.035 1.112 2.180
N 12.000 2100.000 173.500 634.000 702.288 111.041 217.637
H 0.776 2.364 1.800 1.694 0.386 0.061 0.120
J 0.294 0.935 0.636 0.640 0.152 0.024 0.047

1. Permutational Analyses of Variance

Results of univariate PermANOVAs on parameters and multivariate PermANOVA on the whole benthic community are presented in the table below.

Variable Condition Site(Co) Significative groups of similar sites (p > 0.05)
om S S (P1 P2 P3), (P4 R2), (R1 R2 R3)
gravel S (P1 P2 P3 P4 R3 R4), (R1 R2)
sand S All sites in the same group
silt S (P1 P2 P3 P4 R2 R3), (R1 R2), (R1 R4), (R2 R3 R4)
clay S (P1 P2 P3 P4), (P4 R1 R2 R3 R4), (R1 R2 R3), (R3 R4)
arsenic S (P1 P2), (P3 P4 R2), (P3 P4 R1 R3 R4)
cadmium S All except (P1 R2), (P1 R3), (P2 R2), (P2 R3), (P3 R2), (P3 R3)
chromium S (P1 P2 P3 R1 R4), (P4 R2 R3 R4)
copper S S (P1 P2 P3), (P1 P3 P4), (P4 R1 R2), (R1 R2 R3), (R2 R3 R4)
iron All except (P1 R3), (P2 R3), (R1 R3)
manganese S (P1 P2), (P3 P4 R1 R4), (R2 R3)
mercury (P1 P2 P3), (P2 P4 R1 R2 R3 R4)
lead S (P1 P2), (P1 P3), (P4 R1 R2 R3 R4)
zinc S (P1 P2 P3 P4), (P4 R1 R2 R4), (P4 R2 R3 R4)
S (500 µm) S (P1 P2 P3), (P4 R1 R3 R4), (P4 R2 R3 R4)
N (500 µm) S (P1 P2 P3), (P4 R2 R3 R4), (R1 R4)
H (500 µm) All except (P2 P3), (P3 P4)
J (500 µm) All except (P1 P4), (P1 R1), (P2 P3), (P2 P4), (P2 R1), (P2 R2)
ALL SPECIES (500 µm) S S (P1 P2), (R1 R4), (R2 R3)

2. IndVal and SIMPER

These analyses allowed to select species as dependant variables for the regressions. We used results from PRIMER to justify further their choice.

##                          cluster indicator_value probability
## bipalponephtys_neotena         1          0.9490       0.001
## prionospio_steenstrupi         1          0.8969       0.001
## nephtys_sp                     1          0.8494       0.001
## phyllodoce_groenlandica        1          0.8337       0.001
## phoronida                      1          0.7986       0.001
## capitella_sp                   1          0.7940       0.001
## scoloplos_armiger              1          0.7828       0.002
## cirratulidae_spp               1          0.7470       0.001
## limecola_balthica              1          0.7465       0.001
## sarsicytheridea_sp             1          0.6974       0.001
## eteone_sp                      1          0.6386       0.001
## hediste_diversicolor           1          0.5500       0.001
## euchone_analis                 1          0.4500       0.002
## pholoe_longa                   1          0.4015       0.028
## pholoe_sp                      1          0.3792       0.026
## pontoporeia_femorata           1          0.3500       0.015
## podocopida                     1          0.3466       0.013
## diastylis_sculpta              1          0.3435       0.019
## glycera_dibranchiata           1          0.3360       0.008
## axinopsida_orbiculata          1          0.3000       0.016
## praxillella_praetermissa       1          0.3000       0.019
## sabellidae_spp                 1          0.3000       0.018
## tharyx_sp                      1          0.3000       0.024
## maldanidae_spp                 1          0.2500       0.047
## spisula_solidissima            2          0.7515       0.002
## polygordius_sp                 2          0.7397       0.001
## echinarachnius_parma           2          0.7000       0.001
## halacaridae_spp                2          0.2500       0.048
## 
## Sum of probabilities                 =  67.676 
## 
## Sum of Indicator Values              =  24.26 
## 
## Sum of Significant Indicator Values  =  15.78 
## 
## Number of Significant Indicators     =  28 
## 
## Significant Indicator Distribution
## 
##  1  2 
## 24  4
SIMPER results (average dissimilarity: 96.41 )
  average sd ratio ava avb cumsum
bipalponephtys_neotena 0.272 0.152 1.79 425 0.45 0.282
nephtys_sp 0.222 0.143 1.55 345 0.25 0.513
prionospio_steenstrupi 0.0581 0.065 0.895 58 0.2 0.573
scoloplos_armiger 0.0439 0.0524 0.838 63.5 1.4 0.618
spisula_solidissima 0.0398 0.0919 0.433 1.25 19.4 0.66
phoronida 0.0345 0.0372 0.926 56.9 0.1 0.695
phoxocephalus_holbolli 0.0249 0.0535 0.465 4.65 16.2 0.721
polygordius_sp 0.024 0.0971 0.247 0.5 36 0.746
phyllodoce_groenlandica 0.0207 0.0195 1.06 25.5 0.5 0.768
harpacticoida 0.0207 0.0446 0.463 10.9 10.2 0.789
capitella_sp 0.0198 0.0217 0.911 26.2 0.2 0.81
mytilus_sp 0.0153 0.0636 0.241 0.3 15.9 0.826
oligochaeta 0.014 0.0528 0.265 1.5 4.45 0.84
echinarachnius_parma 0.0136 0.0382 0.356 0 6.8 0.854
limecola_balthica 0.0109 0.0175 0.621 10.6 0.05 0.865
hediste_diversicolor 0.0105 0.0416 0.252 2.95 0 0.876
pholoe_minuta_tecta 0.0099 0.0374 0.265 4.95 2.75 0.887
glycera_sp 0.00984 0.0295 0.333 1.35 0 0.897

3. Univariate regressions

Independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices. We used linear models for the all regressions on diversity indices.

3.1. Identification of outliers

To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

Based on Cook’s Distance, we identified stations 1, 19 and 29 as general outliers. They have been deleted for the following analyses.

3.2. Correlations between parameters

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between habitat parameters and metals concentrations
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
om 1 -0.606 -0.137 -0.439 0.649 0.575 0.288 0.175 0.785 -0.066 0.372 0.702 0.641 0.661
gravel -0.606 1 0.236 0.332 -0.754 -0.419 -0.255 -0.162 -0.524 -0.013 -0.384 -0.536 -0.569 -0.607
sand -0.137 0.236 1 -0.644 -0.67 -0.327 -0.512 -0.579 -0.415 -0.545 -0.507 -0.297 -0.456 -0.504
silt -0.439 0.332 -0.644 1 -0.086 -0.143 0.227 0.345 -0.164 0.418 0.072 -0.233 -0.128 -0.099
clay 0.649 -0.754 -0.67 -0.086 1 0.602 0.522 0.476 0.707 0.312 0.624 0.67 0.782 0.809
arsenic 0.575 -0.419 -0.327 -0.143 0.602 1 0.482 0.416 0.681 0.291 0.572 0.584 0.68 0.612
cadmium 0.288 -0.255 -0.512 0.227 0.522 0.482 1 0.855 0.519 0.725 0.822 0.452 0.792 0.775
chromium 0.175 -0.162 -0.579 0.345 0.476 0.416 0.855 1 0.448 0.888 0.82 0.445 0.744 0.719
copper 0.785 -0.524 -0.415 -0.164 0.707 0.681 0.519 0.448 1 0.286 0.587 0.646 0.729 0.837
iron -0.066 -0.013 -0.545 0.418 0.312 0.291 0.725 0.888 0.286 1 0.708 0.174 0.58 0.566
manganese 0.372 -0.384 -0.507 0.072 0.624 0.572 0.822 0.82 0.587 0.708 1 0.591 0.832 0.792
mercury 0.702 -0.536 -0.297 -0.233 0.67 0.584 0.452 0.445 0.646 0.174 0.591 1 0.728 0.659
lead 0.641 -0.569 -0.456 -0.128 0.782 0.68 0.792 0.744 0.729 0.58 0.832 0.728 1 0.914
zinc 0.661 -0.607 -0.504 -0.099 0.809 0.612 0.775 0.719 0.837 0.566 0.792 0.659 0.914 1

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:

  • cadmium, chromium and manganese concentrations (cadmium and manganese deleted)
  • lead and zinc concentrations (zinc deleted)

We also decided to exclude clay content in the regressions, as it tends to increase drasticaly VIFs due to a marginal negative correlation with sand (very high \(R^{2}\)).

3.3. Simple regressions

These analyses have been done to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article.

Adjusted R-squared of simple regressions with all variables
  om gravel sand silt arsenic chromium copper iron mercury lead
S 0.3729 0.1572 0.005039 0.2358 0.3219 0.002971 0.4527 -0.02687 0.3132 0.4404
N 0.4488 0.1816 0.04396 0.2275 0.56 0.1497 0.6473 -0.01284 0.2645 0.7273
H 0.01303 -0.0257 -0.009711 -0.02158 -0.02834 -0.005307 0.01352 0.007732 0.0008677 -0.02508
J 0.03825 0.02916 -0.02281 0.07349 0.1648 0.01825 0.06347 -0.01873 0.01996 0.147
p-values of simple regressions with all variables
  om gravel sand silt arsenic chromium copper iron mercury lead
S 3.576e-05 0.008752 0.2843 0.001363 0.0001491 0.2999 3.047e-06 0.8111 0.0001886 4.546e-06
N 3.466e-06 0.004978 0.1122 0.001671 6.116e-08 0.01038 1.193e-09 0.4658 0.0006679 1.263e-11
H 0.2326 0.7561 0.4242 0.6277 0.9301 0.3743 0.2299 0.2655 0.3168 0.7321
J 0.1279 0.158 0.6597 0.05756 0.007336 0.2048 0.07208 0.5646 0.1965 0.01103

3.4. Multiple regressions

This section presents analyses done (i) to determine which model (metals, parameters or all) describes the best the parameters and (ii) which variables are the most important to explain the parameters.

3.4.1. Best model selection

The aim here is to know which model is the best to explain our data.

Species richness

  n df AIC ∆AIC R2adj
Full model 37 12 231.6 6.506 0.56
Parameters 37 6 234.5 9.361 0.46
Metals 37 8 225.1 0 0.6

Total abundance

  n df AIC ∆AIC R2adj
Full model 37 12 539.8 0.8106 0.81
Parameters 37 6 566.2 27.15 0.56
Metals 37 8 539 0 0.8

Shannon index

  n df AIC ∆AIC R2adj
Full model 37 12 48.1 5.671 -0.08
Parameters 37 6 43.2 0.7641 -0.06
Metals 37 8 42.43 0 0.01

Piélou’s evenness

  n df AIC ∆AIC R2adj
Full model 37 12 -25.59 4.892 0.02
Parameters 37 6 -30 0.4854 0.02
Metals 37 8 -30.49 0 0.08

3.4.2. Significative variables selection

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:

  • for the model with all variables
Variable (or combination) S N H J
om +
gravel
sand/clay -
silt
arsenic -
chromium/cadmium/manganese -
copper +
iron - -
mercury +
lead/zinc + +
Adjusted \(R^{2}\) 0.62 0.83 0.07 0.19
  • for the model with habitat parameters
Variable (or combination) S N H J
om + +
gravel
sand/clay - -
silt - - +
Adjusted \(R^{2}\) 0.47 0.57 0 0.07
  • for the model with heavy metals
Variable (or combination) S N H J
arsenic
chromium/cadmium/manganese - -
copper +
iron -
mercury + +
lead/zinc + + -
Adjusted \(R^{2}\) 0.63 0.81 0.07 0.15

Details of the regressions, with diagnostics and cross-validation, are summarized below.

All variables

Species richness
## FULL MODEL
## Adjusted R2 is: 0.56
Fitting linear model: S ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.02 4.443 3.83 0.0007264 * * *
om -0.6133 1.305 -0.4699 0.6424
gravel -56.93 117.7 -0.4836 0.6327
sand -1.79 3.554 -0.5036 0.6188
silt -63.47 229.5 -0.2766 0.7843
arsenic -0.5324 1.552 -0.3429 0.7344
chromium -0.05353 0.08605 -0.6221 0.5393
copper 0.1134 0.269 0.4213 0.677
iron -6.346e-05 7.791e-05 -0.8145 0.4228
mercury 122.5 81.57 1.502 0.1452
lead 1.728 1.508 1.146 0.2624
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.46 1.47 1.65 1.73 2.49 3.25 2.95 2.47 2.05 5.55 
## REDUCED MODEL
## Adjusted R2 is: 0.62
Fitting linear model: S ~ iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.16 1.956 7.242 2.632e-08 * * *
iron -0.0001034 3.307e-05 -3.126 0.00368 * *
mercury 96.39 42.14 2.287 0.02874 *
lead 1.747 0.3195 5.467 4.644e-06 * * *
Variance Inflation Factors
  iron mercury lead
VIF 1.14 1.14 1.27 
## RMSE for the full model: 6.394255 
## RMSE for the reduced model: 4.693435

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.81
Fitting linear model: N ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -89.25 286 -0.3121 0.7575
om 158.5 84.01 1.886 0.07046
gravel -6505 7576 -0.8586 0.3984
sand -45.64 228.8 -0.1995 0.8434
silt -8128 14772 -0.5503 0.5868
arsenic 135.7 99.93 1.358 0.1861
chromium 8.156 5.539 1.473 0.1528
copper 7.064 17.32 0.408 0.6866
iron -0.01085 0.005015 -2.163 0.03989 *
mercury -4096 5250 -0.7802 0.4423
lead 71.69 97.06 0.7386 0.4668
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.46 1.47 1.65 1.73 2.49 3.25 2.95 2.47 2.05 5.55 
## REDUCED MODEL
## Adjusted R2 is: 0.83
Fitting linear model: N ~ om + iron + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.57 137.4 0.2371 0.8141
om 99.03 41.61 2.38 0.02325 *
iron -0.00665 0.002313 -2.875 0.007019 * *
lead 203.3 23.73 8.568 6.69e-10 * * *
Variance Inflation Factors
  om iron lead
VIF 1.28 1.2 1.42 
## RMSE for the full model: 470.5306 
## RMSE for the reduced model: 331.8861

Shannon index
## FULL MODEL
## Adjusted R2 is: -0.08
Fitting linear model: H ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.199 0.3721 5.91 3.108e-06 * * *
om -0.04909 0.1093 -0.4492 0.657
gravel 2.415 9.856 0.245 0.8083
sand -0.2924 0.2976 -0.9826 0.3348
silt 4.801 19.22 0.2498 0.8047
arsenic -0.1596 0.13 -1.227 0.2307
chromium -0.007351 0.007205 -1.02 0.317
copper 0.01162 0.02253 0.516 0.6102
iron -1.032e-06 6.523e-06 -0.1581 0.8756
mercury 3.87 6.83 0.5666 0.5758
lead 0.08628 0.1263 0.6833 0.5005
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.46 1.47 1.65 1.73 2.49 3.25 2.95 2.47 2.05 5.55 
## REDUCED MODEL
## Adjusted R2 is: 0.07
Fitting linear model: H ~ chromium + copper
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.777 0.1505 11.81 1.394e-13 * * *
chromium -0.004133 0.002351 -1.758 0.08779
copper 0.01578 0.008098 1.949 0.05962
Variance Inflation Factors
  chromium copper
VIF 1.14 1.14 
## RMSE for the full model: 0.6152128 
## RMSE for the reduced model: 0.438194

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8551 0.1374 6.222 1.393e-06 * * *
om -0.02196 0.04037 -0.544 0.5911
gravel 1.956 3.641 0.5372 0.5957
sand -0.1245 0.1099 -1.132 0.2678
silt 2.152 7.098 0.3032 0.7642
arsenic -0.06068 0.04802 -1.264 0.2175
chromium -0.001727 0.002661 -0.6487 0.5222
copper 0.0007926 0.008321 0.09526 0.9248
iron 2.234e-07 2.41e-06 0.0927 0.9268
mercury 0.4984 2.523 0.1975 0.8449
lead 0.01395 0.04664 0.299 0.7673
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.46 1.47 1.65 1.73 2.49 3.25 2.95 2.47 2.05 5.55 
## REDUCED MODEL
## Adjusted R2 is: 0.19
Fitting linear model: J ~ sand + arsenic
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8063 0.05738 14.05 1.007e-15 * * *
sand -0.08921 0.06338 -1.407 0.1684
arsenic -0.05829 0.01837 -3.173 0.003199 * *
Variance Inflation Factors
  sand arsenic
VIF 1.05 1.05 
## RMSE for the full model: 0.1880904 
## RMSE for the reduced model: 0.1384043

Parameters

Species richness
## FULL MODEL
## Adjusted R2 is: 0.46
Fitting linear model: S ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.81 2.026 8.298 1.762e-09 * * *
om 1.902 0.6915 2.751 0.009704 * *
gravel -53.43 108.6 -0.492 0.626
sand -4.354 3.012 -1.446 0.158
silt -470.2 207.3 -2.268 0.03018 *
Variance Inflation Factors
  om gravel sand silt
VIF 1.18 1.23 1.27 1.42 
## REDUCED MODEL
## Adjusted R2 is: 0.47
Fitting linear model: S ~ om + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.83 2.002 8.408 1.029e-09 * * *
om 1.911 0.6833 2.797 0.008531 * *
sand -4.963 2.713 -1.829 0.07639
silt -519.7 179.1 -2.902 0.006557 * *
Variance Inflation Factors
  om sand silt
VIF 1.18 1.16 1.24 
## RMSE for the full model: 5.770942 
## RMSE for the reduced model: 5.752928

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.56
Fitting linear model: N ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 696.2 179.1 3.887 0.0004805 * * *
om 205.5 61.14 3.361 0.002021 * *
gravel -4617 9600 -0.4809 0.6339
sand -582.5 266.2 -2.188 0.03609 *
silt -47037 18323 -2.567 0.01513 *
Variance Inflation Factors
  om gravel sand silt
VIF 1.18 1.23 1.27 1.42 
## REDUCED MODEL
## Adjusted R2 is: 0.57
Fitting linear model: N ~ om + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 698.1 177 3.945 0.0003934 * * *
om 206.3 60.4 3.416 0.001705 * *
sand -635.2 239.8 -2.649 0.0123 *
silt -51316 15829 -3.242 0.002714 * *
Variance Inflation Factors
  om sand silt
VIF 1.18 1.16 1.24 
## RMSE for the full model: 589.8205 
## RMSE for the reduced model: 589.1765

Shannon index
## FULL MODEL
## Adjusted R2 is: -0.06
Fitting linear model: H ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.672 0.1527 10.95 2.37e-12 * * *
om 0.03866 0.05214 0.7414 0.4638
gravel 3.286 8.187 0.4013 0.6909
sand -0.1739 0.2271 -0.7657 0.4495
silt -7.438 15.63 -0.476 0.6373
Variance Inflation Factors
  om gravel sand silt
VIF 1.18 1.23 1.27 1.42 
## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: H ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.681 0.06334 26.54 3.127e-25 * * *

Quitting from lines 413-417 (C1_analyses_loc2.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 26 warnings (use warnings() to see them)

## RMSE for the full model: 0.4727543 
## RMSE for the reduced model: 0.3973865

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6323 0.0568 11.13 1.542e-12 * * *
om -0.01567 0.01939 -0.8083 0.4249
gravel 1.959 3.045 0.6434 0.5246
sand -0.03079 0.08444 -0.3647 0.7177
silt 4.217 5.811 0.7256 0.4734
Variance Inflation Factors
  om gravel sand silt
VIF 1.18 1.23 1.27 1.42 
## REDUCED MODEL
## Adjusted R2 is: 0.07
Fitting linear model: J ~ silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5996 0.03013 19.9 1.159e-20 * * *
silt 7.824 3.985 1.964 0.05756
Variance Inflation Factors
  silt
VIF 1 
## RMSE for the full model: 0.1648217 
## RMSE for the reduced model: 0.1450135

Metals

Species richness
## FULL MODEL
## Adjusted R2 is: 0.6
Fitting linear model: S ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.45 2.672 5.407 7.373e-06 * * *
arsenic -0.4785 1.232 -0.3883 0.7005
chromium -0.06975 0.07048 -0.9897 0.3302
copper 0.0891 0.2007 0.444 0.6602
iron -4.065e-05 6.949e-05 -0.585 0.5629
mercury 96.65 44.02 2.196 0.03598 *
lead 1.927 0.9541 2.02 0.05241
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 2.8 2.32 2.32 1.16 3.7 
## REDUCED MODEL
## Adjusted R2 is: 0.63
Fitting linear model: S ~ chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.46 1.766 7.624 8.952e-09 * * *
chromium -0.1056 0.0328 -3.22 0.002879 * *
mercury 105.1 41.38 2.54 0.01599 *
lead 2.048 0.3687 5.557 3.568e-06 * * *
Variance Inflation Factors
  chromium mercury lead
VIF 1.35 1.13 1.48 
## RMSE for the full model: 5.937329 
## RMSE for the reduced model: 4.702199

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.8
Fitting linear model: N ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 93.14 185.7 0.5014 0.6197
arsenic 28.59 85.66 0.3337 0.7409
chromium 5.033 4.899 1.027 0.3125
copper 3.664 13.95 0.2627 0.7946
iron -0.01197 0.004831 -2.478 0.01907 *
mercury 4364 3060 1.426 0.1641
lead 182.9 66.33 2.758 0.009804 * *
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 2.8 2.32 2.32 1.16 3.7 
## REDUCED MODEL
## Adjusted R2 is: 0.81
Fitting linear model: N ~ iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 131 135.5 0.9664 0.3409
iron -0.007999 0.002292 -3.491 0.001391 * *
mercury 4475 2920 1.532 0.135
lead 222.2 22.14 10.03 1.484e-11 * * *
Variance Inflation Factors
  iron mercury lead
VIF 1.14 1.14 1.27 
## RMSE for the full model: 355.2602 
## RMSE for the reduced model: 319.5343

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.01
Fitting linear model: H ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.882 0.2263 8.317 2.776e-09 * * *
arsenic -0.09566 0.1043 -0.9167 0.3666
chromium -0.004541 0.005968 -0.7609 0.4526
copper 0.0236 0.01699 1.389 0.1751
iron 1.064e-06 5.885e-06 0.1807 0.8578
mercury 2.052 3.727 0.5505 0.586
lead 0.001615 0.0808 0.01999 0.9842
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 2.8 2.32 2.32 1.16 3.7 
## REDUCED MODEL
## Adjusted R2 is: 0.07
Fitting linear model: H ~ chromium + copper
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.777 0.1505 11.81 1.394e-13 * * *
chromium -0.004133 0.002351 -1.758 0.08779
copper 0.01578 0.008098 1.949 0.05962
Variance Inflation Factors
  chromium copper
VIF 1.14 1.14 
## RMSE for the full model: 0.5384085 
## RMSE for the reduced model: 0.438194

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.08
Fitting linear model: J ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7225 0.08446 8.555 1.518e-09 * * *
arsenic -0.02966 0.03895 -0.7614 0.4524
chromium -0.0002512 0.002228 -0.1128 0.911
copper 0.006688 0.006343 1.054 0.3001
iron 1.001e-06 2.197e-06 0.4555 0.652
mercury -0.3539 1.391 -0.2544 0.8009
lead -0.02863 0.03016 -0.9492 0.3501
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 2.8 2.32 2.32 1.16 3.7 
## REDUCED MODEL
## Adjusted R2 is: 0.15
Fitting linear model: J ~ lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7272 0.04071 17.86 3.629e-19 * * *
lead -0.02104 0.007838 -2.684 0.01103 *
Variance Inflation Factors
  lead
VIF 1 
## RMSE for the full model: 0.1705679 
## RMSE for the reduced model: 0.1447149

4. Multivariate regression

Independant variables are habitat parameters and heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.

This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

Elliot Dreujou

2019-12-14